Connected Life Conference July 3, 2014

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1 Nir Kshetri Professor, University of North Carolina Greensboro Connected Life Conference July 3, 2014 Connected Life Conference, July 3,

2 Introduction Outline Principal findings of surveys conducted with businesses/consumers regarding perceptions of/responses to big data Benefits, costs, and externalities of big data Characteristics of big data in relation to privacy/security Discussion and conclusion Connected Life Conference, July 3,

3 Big data: security/privacy issues Huge amount of data: security breaches/privacy violations severe consequences/losses Reputational damage Legal liability Ethical harms and other issues. A large proportion of data: high-velocity/fast data (clickstream/gps data) Stiff resistance from consumers. Companies questionable data collection and sharing practices. Nissan s Leaf brand. iphones and Android: information about users locations. Connected Life Conference, July 3,

4 Big data: security/privacy issues Data in multiple formats A large variety of information More difficult to detect security breaches React appropriately and respond to attacks Unstructured data: Special concerns 61% CAGR. More likely to conceal PII Processes /technologies for securing data: nascent phase Governance issues not addressed. Connected Life Conference, July 3,

5 Principal findings of surveys conducted with businesses/consumers regarding perceptions of/responses to big data Connected Life Conference, July 3,

6 Findings of surveys conducted with businesses Survey conducted by Software specialist, Infor matica Conducted/r eleased in Sample IT and business professionals Major findings Data security and privacy raised concerns for 38%. BARC Institute Second half of business/it decision-makers (Ger., Aus., Swit., Fra., U.K.) 25% respondents expected to encounter data privacy issues. Information Systems Audit and Control Association (ISACA) 2013 IT Risk/Reward Barometer 2,013 Australian and New Zealand IT professionals 5% very prepared to ensure effective governance and privacy, 45% adequately prepared, 25% not prepared at all. Connected Life Conference, July 3,

7 Findings of surveys conducted with businesses Survey conducted by Voltage Security Conducted/r eleased in April 2013 at InfoSecurity Europe Sample >300 seniorlevel professionals Major findings 76%: inability to secure data. 56%: could not start/finish cloud/big data projects due to security concerns. SAP 2014 (at 300 mobile 38%: security and privacy prevented GSMA Mobile operators, fixed organizations from fully unlocking big World Congress) telecomm providers/ other executives data s potential. Ovum (sponsored by Vormetric) Early IT decisionmakers (midand large-sized org.(u.k., Fra., Ger.) 53% concerned about the security issues. Connected Life Conference, July 3,

8 Findings of surveys conducted with consumers Survey conducted by Cable Forum (cableforum.co. uk) Pew Internet & American Life Project. BCG Conducted/ released in Sample Major findings 2008 Forum visitors 95%: would opt out of monitoring (even anonymous) of online activities by a third party (March National 30% smartphone owners: turned off 15-April 3). survey: 2,254 location tracking features U.S. adults 2013 Global 10,000 Privacy of personal data was a top Consumer consumers in 12 issue for 75%. Only 7% were willing to Sentiment countries allow their information to be used for Survey. purposes other than it was originally collected Ovum ,000 people across 11 countries 68%: would use a do-not-track feature if easily available on a search engine. Only 14% believed Internet companies were honest about the use of personal data Connected Life Conference, July 3,

9 Benefits, costs, and externalities of big data Connected Life Conference, July 3,

10 Social and economic benefits and positive externalities Economic efficiency, access to social services, strengthen security, personalize services Mapping apps. Organizational efficiency BMW: Data from test drives of prototypes, workshop reports and other sources Potential issues/vulnerabilities quickly Eliminates before new models are launched. Analyze some data from several months to a few days. Issue repair instructions on a timely basis: Less workshop visits and the time required to repair. Dannon: forecast demands of retailers more accurately, Less wasted product, Higher consumer satisfaction/ profitability. Connected Life Conference, July 3,

11 Social and economic benefits and positive externalities Scientists: improve human well-being. Life science and medical fields: patient data Drug interactions/design and implement optimal drug therapies Effective drug regulation. Reduce negative externalities of unsafe and ineffective drugs Improve the performance of services provided by government agencies reduce t/axpayer costs Strengthened security: law enforcement agencies Quicker response and visible presence in crime prone areas. Fight spread of communicable diseases: Cholera outbreak in Haiti response could have been improved Connected Life Conference, July 3,

12 Use of big data to tailor pricing/product offerings Positive effects on consumer welfare and an increase in the profit. Information goods: distinct products: versioning and differential pricing. Reduction of deadweight losses Non-big data environment: exogenous and observable characteristics Big data analytics: identify variables with a much higher correlation. Shiller s analysis of Netflix : demographic variables + web-browsing history to tailor prices: Variable profits 1.39% compared to 2 nd deg. discrimination Only demographic variables: 0.14%. Some consumers would be charged > 2X. Improved forecasting of the prob. individuals subscription Without any information: 16%. Standard demographics: 6% - 30%. Full set of variables: ~ zero to 91%. New technologies: Google s patent on a technology-- dynamically price electronic contents based on consumer profiling. Connected Life Conference, July 3,

13 Social and economic costs and negative externalities Failure to secure data: amplified technical impact Inappropriate use: psychological/emotional/economic/social harm. Accuracy of predictions of a customer s purchasing requirements or preferences. Highly customized offerings: unpleasant/frightening exp. predictive privacy harm (Crawford & Schultz, 2013). Target s tracking of high school aged girl Re-identification: aggregation process--semi-anonymous or personally non-identifiable info. into non-anonymous or PII. Connected Life Conference, July 3,

14 Big data s challenges on the FIPs Personal data can be put to new uses to create value Often violate the transparency principle of FIPs. Innovations: multiple sources and transferring to third parties. Direct Marketing Association: > US$150b generated using individual data > 70%: exchanging data among firms in the VDN. Convenient/possibly false assumption: making data anonymous before sharing would make it impossible to identify. Failure to protect PII and unintended/inappropriate disclosure: violate the security provision of FIPs. Identified person may suffer physical, psychological, or economic harm: 2011: Customers of Walgreens filed a lawsuit. Machine-to-machine transactions and passive collection without overt interaction (e.g., location data from mobile devices, cookies) The lack of individual consent for collection, use, and dissemination: violation of the individual participation principle. Connected Life Conference, July 3,

15 Characteristics of big data in relation to privacy/security Connected Life Conference, July 3,

16 Big data characteristics and security/privacy Characteristic Collection/storing Sharing/accessibility by third parties and various user types Volume Attract a great deal of attention from cybercriminals. Amplified technical impact Firms may need to outsource to CSPs which may give rise to privacy and security issues. Velocity (Fast Data) Increasing concerns over privacy in behavioral ad based on real-time profiling and tracking technologies such as cookies. Supply of location-based real time personal information; negative spillover effects (e.g., stalking people in real time). Physical security risks. Variety Unstructured data: conceal PII. A large variety of information would make it more difficult to detect security breaches, react appropriately and respond to attacks Lack of mechanisms to ensure that permanent and temporary employees and third-parties have appropriate access to unstructured data and they are in compliance with data protection regulations Connected Life Conference, July 3,

17 Big data characteristics and security/privacy Characteristic Collection/storing Sharing/accessibility by third parties and various user types Variability Lack of capabilities to securely store huge amounts of data and manage collected data during peak data traffic. Peak data traffic may cause higher needs to outsource to CSPs which give rise to important privacy and security issues. Attractiveness as a crime target increases during peak data traffic. Complexity Resulting data is often more personal than the set of data the person would consent to give. Data collected from illicit sources: more likely to have info. on technologically less savvy consumers A party with whom de-identified personal data is shared may combine data from other sources to re-identify. Connected Life Conference, July 3,

18 Discussion and conclusion Connected Life Conference, July 3,

19 Discussion and conclusion Risks of owning/storing: increase with the size, variety and complexity. Often high risk in unstructured data Higher risks for data obtained from illicit sources Volume of data. Target case: higher risks during peak data traffic periods. Create value-- share /make accessible to various entities. Responsible for wrongdoing by third parties and various user types Shared data may fall into the hands of illegal/malicious actors Level of resources in proactively managing security/privacy: Assessment of the big data environment in terms of the abovementioned parameters. Connected Life Conference, July 3,

20 Discussion and conclusion--contd Proactive risk management: understanding root causes. Examine sources: avoiding illicit sources. Emerging new threats: data sharing and accessibility issues. Insider threats. Vormetric: 3% of U.S./9% of European organizations felt safe against insider threats. Especially pronounced for unstructured data Information on all the parties who have access data. Illicit/grey area businesses accessing data on consumers 2008: Phorm s deals with British ISPs: BT, Virgin Media and TalkTalk. Phorm: operated 121Media and distributed PeopleOnPage Spyware according to F-Secure. 20 Connected Life Conference, July 3, 2014

21 Discussion and conclusion----contd Regulatory gray area : no adequate consumer protection Ethical standards/codes of conduct: not well established Most companies lack best practices/privacy policies Illicit and gray area actors: draw on the same data sets and information as legitimate users of big data. Dangerous, creepy and annoying practices: not illegal. I-Free: Girls Around Me provided nothing more than data that was publicly available on Foursquare and Facebook. Consumers can help develop formal/informal institutions Lawsuits, filing of complaints with regulatory bodies Formal pressures on businesses: use responsibly and ethically. Connected Life Conference, July 3,

22 Thank you! Connected Life Conference, July 3,

Made available courtesy of Elsevier: http://dx.doi.org/10.1016/j.telpol.2014.10.002

Made available courtesy of Elsevier: http://dx.doi.org/10.1016/j.telpol.2014.10.002 Big Data s Impact on Privacy, Security and Consumer Welfare By: Nir Kshetri Kshetri, N. (2014). Big data's impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145.

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